Mar, Arthur

Publications, Activities, and Awards

• Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods
• Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
• Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
• Accelerating the Discovery of Materials: Machine-Learning Approach
• Accelerating the Discovery of Solid State Materials
• Accelerating the Discovery of Solid State Materials
• Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the discovery of solid state materials: From traditional to machine- learning approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
• Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting Biomineralization
• Classification of crystal structures: Machine-learning predictions and experimental validation
• Classification of Half-Heusler Compounds through Machine Learning Approaches
• Classification of Half-Heusler Compounds through Machine-Learning Approaches
• Coloured intermetallic compounds Li2ZnGa and Li2ZnIn
• Coloured intermetallic compounds LiCu2Al and LiCu2Ga
• Comparison of computational and experimental inorganic crystal structures
• Comparison of computational and experimental inorganic crystal structures
• Computational workshop
• Crystallography in Chemistry and Materials Science
• Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency Optimization
• Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
• Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach
• Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proof
• Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC
• Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency Optimization
• Excellence in Undergraduate Teaching
• Excellence in Undergraduate Teaching
• Experimental Validation of High Thermoelectric Performance in RECuZnP2 Predicted by High-Throughput DFT Calculations
• Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculations
• Exploring the colours of gold alloys with machine learning
• Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation
• Hexagonal Double Perovskite Cs2AgCrCl6
• High-Throughput Approaches for Discovering Thermoelectric Materials
• How large is an atom?
• How to look for compounds
• How to look for compounds
• How to look for compounds
• How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
• In Search of Coloured Intermetallics
• Influence of hidden halogen mobility on local structure of CsSn(Cl1-xBrx)3 mixed-halide perovskites by solid-state NMR
• Introduction to Machine Learning: A Practical Workshop
• Investigation of Li-Zn-X (X = Ga, In) coloured intermetallics
• Invited talk: ACS national meeting, Boston
• Lost horses on the frontier: K2BiCl5 and K3Bi2Br9
• Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
• Machine learning in solid-state chemistry: Heusler compounds
• Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler Structures
• Machine-Learning Predictions and Experimental Validation of Full and Half-Heusler Structures
• Machine-learning predictions and experimental validation of full- and half-Heusler structures
• Machine-Learning Predictions and Experimental Validation of Heusler Structures
• Machine-learning predictions of half-Heusler structures
• Mere anarchy is loosed: Structural disorder in Cu2Zn1-xCdxSnS4
• Minority report: Structure and bonding of YbNi3Ga9 and YbCu3Ga8 obtained in gallium flux
• Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity
• Optimization of Organic Solar Cell Efficiency via Machine Learning and Design of Experiments
• Predicting noncentrosymmetric quaternary tellurides using machine learning
• Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach
• Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)
• Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)
• Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)
• Rare-earth indium selenides RE3InSe6 (RE = La-Nd, Sm, Gd, Tb): Structural evolution from tetrahedral to octahedral sites
• Rare-earth transition-metal oxychalcogenides
• Rare-earth transition-metal oxyselenides
• Revealing the Local Sn and Pb Arrangements in CsSnxPb1–xBr3 Perovskites with Solid-State NMR Spectroscopy
• Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf–In System
• Semiconducting Sm3GaSe5O with trigonal bipyramidal GaSe5 units
• Solving the Colouring Problem in Half- Heusler Structures: Machine-Learning Predictions and Experimental Validation
• Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation
• Structure and Luminescence Properties of Rare-Earth Chalcohalides RE3Ge2Ch8X (Ch = S, Se; X = Cl, Br, I)
• Students' Choice Honour Roll
• Symmetry in art and chemistry; machine learning
• Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)
• Ternary and Quaternary Rare-Earth Transition-Metal Germanides
• Ternary and quaternary rare‐earth germanides: discovery of intermetallic compounds from traditional to machine‐learning approaches
• Ternary Germanides in Ce-M-Ge System (M=Rh, Co)
• Ternary Germanides in the Ce–M–Ge (M = Rh, Co) Systems
• Ternary phases in the Yb-Cu-Ga and Yb-Ni-Ga systems
• Ternary rare-earth-metal nickel indides RE23Ni7In4 (RE = Gd, Tb, Dy) with Yb23Cu7Mg4-type structure
• Thermoelectric properties of inverse perovskites A3TtO (A = Mg, Ca; Tt = Si, Ge): Computational and experimental investigations
• Three Rh-rich ternary germanides in the Ce-Rh-Ge system
• USchool: Materials and Informatics
• USchool: Materials and Informatics
• X-ray diffraction short course